

'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence([1, 2, 3]   )   )
   self.assertEqual(arr.type  , pa.intaa()   )







 def 						_A(       self   ):
   with self.assertRaises(snake_case_   ):
     lowercase									=pa.array(TypedSequence([1, 2, 3]   )  , type=pa.intaa()   )







 def 						_A(       self   ):
   with self.assertRaises(snake_case_   ):
     lowercase									=pa.array(TypedSequence([1, 2, 3]  , try_type=Value('''bool'''   )  , type=Value('''int64'''   )   )   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence([1, 2, 3]  , type=Value('''int32'''   )   )   )
   self.assertEqual(arr.type  , pa.intaa()   )







 def 						_A(       self   ):
   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
     lowercase									=pa.array(TypedSequence(['''foo''', '''bar''']  , type=Value('''int64'''   )   )   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence([1, 2, 3]  , try_type=Value('''int32'''   )   )   )
   self.assertEqual(arr.type  , pa.intaa()   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence(['''foo''', '''bar''']  , try_type=Value('''int64'''   )   )   )
   self.assertEqual(arr.type  , pa.string()   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence([[[1, 2, 3]]]  , type=ArrayaD((1, 3)  , '''int64'''   )   )   )
   self.assertEqual(arr.type  , ArrayaDExtensionType((1, 3)  , '''int64'''   )   )







 def 						_A(       self   ):
   with self.assertRaises((TypeError, pa.lib.ArrowInvalid)   ):
     lowercase									=pa.array(TypedSequence(['''foo''', '''bar''']  , type=ArrayaD((1, 3)  , '''int64'''   )   )   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence([[[1, 2, 3]]]  , try_type=ArrayaD((1, 3)  , '''int64'''   )   )   )
   self.assertEqual(arr.type  , ArrayaDExtensionType((1, 3)  , '''int64'''   )   )







 def 						_A(       self   ):
   lowercase									=pa.array(TypedSequence(['''foo''', '''bar''']  , try_type=ArrayaD((1, 3)  , '''int64'''   )   )   )
   self.assertEqual(arr.type  , pa.string()   )







 @require_pil
 def 						_A(       self   ):
   import PIL.Image

   lowercase									=PIL.Image.fromarray(np.arange(10  , dtype=np.uinta   ).reshape(2  , 5   )   )
   with patch(
       '''datasets.arrow_writer.cast_to_python_objects'''  , side_effect=snake_case_   ) as mock_cast_to_python_objects:
     lowercase									=pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image]  , type=Image()   )   )
     lowercase							, lowercase									=mock_cast_to_python_objects.call_args_list[-1]
     self.assertIn('''optimize_list_casting'''  , snake_case_   )
     self.assertFalse(kwargs['''optimize_list_casting''']   )




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    int	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=pa.BufferReader(lowercase_	) if isinstance(lowercase_     ,					pa.Buffer	) else pa.memory_map(lowercase_	)
  lowercase									=pa.ipc.open_stream(lowercase_	)
  lowercase									=f.read_all()
  assert len(pa_table.to_batches()	) == expected_num_chunks
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
  del pa_table




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 1, 1_0]	)
@pytest.mark.parametrize(
    '''fields'''     ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    Optional[Any]	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  lowercase									=pa.schema(lowercase_	) if fields else None
  with ArrowWriter(stream=lowercase_     ,					schema=lowercase_     ,					writer_batch_size=lowercase_	) as writer:
    writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
    writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowercase									={'''col_1''': pa.string(), '''col_2''': pa.intaa()}
  assert writer._schema == pa.schema(lowercase_     ,					metadata=writer._schema.metadata	)
  _check_output(output.getvalue()     ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




def 	UpperCamelCase   (       )		->	int:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  lowercase									=Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']	)}	)
  with ArrowWriter(stream=lowercase_     ,					features=lowercase_	) as writer:
    writer.write({'''labels''': 0}	)
    writer.write({'''labels''': 1}	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert writer._schema == features.arrow_schema
  assert writer._schema.metadata == features.arrow_schema.metadata
  lowercase									=pa.BufferReader(output.getvalue()	)
  lowercase									=pa.ipc.open_stream(lowercase_	)
  lowercase									=f.read_all()
  lowercase									=pa_table.schema
  assert pa_table.num_rows == 2
  assert schema == features.arrow_schema
  assert schema.metadata == features.arrow_schema.metadata
  assert features == Features.from_arrow_schema(lowercase_	)




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 1, 1_0]	)
def 	UpperCamelCase   (       lowercase_						:    List[str]	)		->	str:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  with ArrowWriter(
      stream=lowercase_     ,					writer_batch_size=lowercase_     ,					hash_salt='''split_name'''     ,					check_duplicates=lowercase_     ,					) as writer:
    with pytest.raises(lowercase_	):
      writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,					key=[1, 2]	)
      lowercase							, lowercase									=writer.finalize()




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 2, 1_0]	)
def 	UpperCamelCase   (       lowercase_						:    Optional[Any]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  with ArrowWriter(
      stream=lowercase_     ,					writer_batch_size=lowercase_     ,					hash_salt='''split_name'''     ,					check_duplicates=lowercase_     ,					) as writer:
    with pytest.raises(lowercase_	):
      writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,					key=1_0	)
      writer.write({'''col_1''': '''bar''', '''col_2''': 2}     ,					key=1_0	)
      lowercase							, lowercase									=writer.finalize()




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 2, 1_0]	)
def 	UpperCamelCase   (       lowercase_						:    List[Any]	)		->	Any:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  with ArrowWriter(
      stream=lowercase_     ,					writer_batch_size=lowercase_     ,					hash_salt='''split_name'''     ,					check_duplicates=lowercase_     ,					) as writer:
    writer.write({'''col_1''': '''foo''', '''col_2''': 1}     ,					key=1	)
    writer.write({'''col_1''': '''bar''', '''col_2''': 2}     ,					key=2	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  _check_output(output.getvalue()     ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 1, 1_0]	)
@pytest.mark.parametrize(
    '''fields'''     ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Union[str, Any]	)		->	Any:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  lowercase									=pa.schema(lowercase_	) if fields else None
  with ArrowWriter(stream=lowercase_     ,					schema=lowercase_     ,					writer_batch_size=lowercase_	) as writer:
    writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)
    writer.write_batch({'''col_1''': [], '''col_2''': []}	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowercase									={'''col_1''': pa.string(), '''col_2''': pa.intaa()}
  assert writer._schema == pa.schema(lowercase_     ,					metadata=writer._schema.metadata	)
  _check_output(output.getvalue()     ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 1, 1_0]	)
@pytest.mark.parametrize(
    '''fields'''     ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    List[str]	)		->	int:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  lowercase									=pa.schema(lowercase_	) if fields else None
  with ArrowWriter(stream=lowercase_     ,					schema=lowercase_     ,					writer_batch_size=lowercase_	) as writer:
    writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowercase									={'''col_1''': pa.string(), '''col_2''': pa.intaa()}
  assert writer._schema == pa.schema(lowercase_     ,					metadata=writer._schema.metadata	)
  _check_output(output.getvalue()     ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




@pytest.mark.parametrize('''writer_batch_size'''     ,					[None, 1, 1_0]	)
@pytest.mark.parametrize(
    '''fields'''     ,					[None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]	)
def 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    Tuple	)		->	int:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  lowercase									=pa.schema(lowercase_	) if fields else None
  with ArrowWriter(stream=lowercase_     ,					schema=lowercase_     ,					writer_batch_size=lowercase_	) as writer:
    writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}	)	)
    writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}	)	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  if not fields:
    lowercase									={'''col_1''': pa.string(), '''col_2''': pa.intaa()}
  assert writer._schema == pa.schema(lowercase_     ,					metadata=writer._schema.metadata	)
  _check_output(output.getvalue()     ,					expected_num_chunks=num_examples if writer_batch_size == 1 else 1	)




def 	UpperCamelCase   (       )		->	Union[str, Any]:




  '''simple docstring'''



  with tempfile.TemporaryDirectory() as tmp_dir:
    lowercase									={'''col_1''': pa.string(), '''col_2''': pa.intaa()}
    lowercase									=os.path.join(lowercase_     ,					'''test.arrow'''	)
    with ArrowWriter(path=lowercase_     ,					schema=pa.schema(lowercase_	)	) as writer:
      writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}	)
      lowercase							, lowercase									=writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == pa.schema(lowercase_     ,					metadata=writer._schema.metadata	)
    _check_output(lowercase_     ,					1	)




def 	UpperCamelCase   (       lowercase_						:    List[str]	)		->	Dict:




  '''simple docstring'''



  if pa.types.is_list(lowercase_	):
    return get_base_dtype(arr_type.value_type	)
  else:
    return arr_type




def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    str	)		->	Optional[int]:




  '''simple docstring'''



  if isinstance(lst[0]     ,					lowercase_	):
    change_first_primitive_element_in_list(lst[0]     ,					lowercase_	)
  else:
    lowercase									=value




@pytest.mark.parametrize('''optimized_int_type, expected_dtype'''     ,					[(None, pa.intaa()), (Value('''int32'''	), pa.intaa())]	)
@pytest.mark.parametrize('''sequence'''     ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Optional[int]	)		->	Optional[Any]:




  '''simple docstring'''



  lowercase									=pa.array(TypedSequence(lowercase_     ,					optimized_int_type=lowercase_	)	)
  assert get_base_dtype(arr.type	) == expected_dtype




@pytest.mark.parametrize(
    '''col, expected_dtype'''     ,					[
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ]     ,					)
@pytest.mark.parametrize('''sequence'''     ,					[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]	)
def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    List[Any]     ,					lowercase_						:    Union[str, Any]	)		->	Any:




  '''simple docstring'''



  lowercase									=pa.array(OptimizedTypedSequence(lowercase_     ,					col=lowercase_	)	)
  assert get_base_dtype(arr.type	) == expected_dtype

  # not in range
  if col != "other":
    # avoids errors due to in-place modifications
    lowercase									=copy.deepcopy(lowercase_	)
    lowercase									=np.iinfo(expected_dtype.to_pandas_dtype()	).max + 1
    change_first_primitive_element_in_list(lowercase_     ,					lowercase_	)
    lowercase									=pa.array(OptimizedTypedSequence(lowercase_     ,					col=lowercase_	)	)
    assert get_base_dtype(arr.type	) == pa.intaa()




@pytest.mark.parametrize('''raise_exception'''     ,					[False, True]	)
def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Tuple	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=str(tmp_path / '''dataset-train.arrow'''	)
  try:
    with ArrowWriter(path=lowercase_	) as writer:
      if raise_exception:
        raise pa.lib.ArrowInvalid()
      else:
        writer.stream.close()
  except pa.lib.ArrowInvalid:
    pass
  finally:
    assert writer.stream.closed




def 	UpperCamelCase   (       lowercase_						:    List[Any]	)		->	str:




  '''simple docstring'''



  lowercase									='''mock://dataset-train.arrow'''
  with ArrowWriter(path=lowercase_     ,					storage_options=mockfs.storage_options	) as writer:
    assert isinstance(writer._fs     ,					type(lowercase_	)	)
    assert writer._fs.storage_options == mockfs.storage_options
    writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
    writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  assert mockfs.exists(lowercase_	)




def 	UpperCamelCase   (       )		->	Optional[int]:




  '''simple docstring'''



  lowercase									=pa.BufferOutputStream()
  with ParquetWriter(stream=lowercase_	) as writer:
    writer.write({'''col_1''': '''foo''', '''col_2''': 1}	)
    writer.write({'''col_1''': '''bar''', '''col_2''': 2}	)
    lowercase							, lowercase									=writer.finalize()
  assert num_examples == 2
  assert num_bytes > 0
  lowercase									=pa.BufferReader(output.getvalue()	)
  lowercase									=pq.read_table(lowercase_	)
  assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}




@require_pil
@pytest.mark.parametrize('''embed_local_files'''     ,					[False, True]	)
def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Any	)		->	str:




  '''simple docstring'''



  import PIL.Image

  lowercase									=str(tmp_path / '''test_image_rgb.jpg'''	)
  PIL.Image.fromarray(np.zeros((5, 5)     ,					dtype=np.uinta	)	).save(lowercase_     ,					format='''png'''	)
  lowercase									=pa.BufferOutputStream()
  with ParquetWriter(
      stream=lowercase_     ,					features=Features({'''image''': Image()}	)     ,					embed_local_files=lowercase_	) as writer:
    writer.write({'''image''': image_path}	)
    writer.finalize()
  lowercase									=pa.BufferReader(output.getvalue()	)
  lowercase									=pq.read_table(lowercase_	)
  lowercase									=pa_table.to_pydict()
  if embed_local_files:
    assert isinstance(out['''image'''][0]['''path''']     ,					lowercase_	)
    with open(lowercase_     ,					'''rb'''	) as f:
      assert out["image"][0]["bytes"] == f.read()
  else:
    assert out["image"][0]["path"] == image_path
    assert out["image"][0]["bytes"] is None




def 	UpperCamelCase   (       )		->	str:




  '''simple docstring'''



  lowercase									=pa.schema([pa.field('''col_1'''     ,					pa.string()     ,					nullable=lowercase_	)]	)
  lowercase									=pa.BufferOutputStream()
  with ArrowWriter(stream=lowercase_	) as writer:
    writer._build_writer(inferred_schema=lowercase_	)
    assert writer._schema == pa.schema([pa.field('''col_1'''     ,					pa.string()	)]	)








'''simple docstring'''





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  snake_case__      (					UpperCamelCase):
					a_        =					["image_processor", "tokenizer"]
					a_        =					"LayoutLMv2ImageProcessor"
					a_        =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")




					def __init__(  self  :		Optional[int]				, _A  :		str=None				, _A  :		Optional[Any]=None				, **_A  :		Any      )    ->							Tuple:
								if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				, _A				, )
											UpperCAmelCase_      :       int          =       kwargs.pop('''feature_extractor'''      )

								UpperCAmelCase_      :       str          =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''      )
								if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''      )

								super().__init__(_A				, _A      )




					def __call__(  self  :		str				, _A  :		Optional[int]				, _A  :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				, _A  :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				, _A  :		Union[List[List[int]], List[List[List[int]]]] = None				, _A  :		Optional[Union[List[int], List[List[int]]]] = None				, _A  :		bool = True				, _A  :		Union[bool, str, PaddingStrategy] = False				, _A  :		Union[bool, str, TruncationStrategy] = None				, _A  :		Optional[int] = None				, _A  :		int = 0				, _A  :		Optional[int] = None				, _A  :		Optional[bool] = None				, _A  :		Optional[bool] = None				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = False				, _A  :		bool = True				, _A  :		Optional[Union[str, TensorType]] = None				, **_A  :		Dict				, )    ->							BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    '''You cannot provide bounding boxes '''
											    '''if you initialized the image processor with apply_ocr set to True.'''      )

								if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''      )

								if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''      )

								# first, apply the image processor
								UpperCAmelCase_      :       int          =       self.image_processor(images=_A				, return_tensors=_A      )

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(_A				, _A      ):
														UpperCAmelCase_      :       int          =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
											UpperCAmelCase_      :       int          =       features['''words''']

								UpperCAmelCase_      :       str          =       self.tokenizer(
								    text=text if text is not None else features['''words''']				, text_pair=text_pair if text_pair is not None else None				, boxes=boxes if boxes is not None else features['''boxes''']				, word_labels=_A				, add_special_tokens=_A				, padding=_A				, truncation=_A				, max_length=_A				, stride=_A				, pad_to_multiple_of=_A				, return_token_type_ids=_A				, return_attention_mask=_A				, return_overflowing_tokens=_A				, return_special_tokens_mask=_A				, return_offsets_mapping=_A				, return_length=_A				, verbose=_A				, return_tensors=_A				, **_A				, )

								# add pixel values
								UpperCAmelCase_      :       int          =       features.pop('''pixel_values'''      )
								if return_overflowing_tokens is True:
											UpperCAmelCase_      :       List[Any]          =       self.get_overflowing_images(_A				, encoded_inputs['''overflow_to_sample_mapping''']      )
								UpperCAmelCase_      :       Optional[int]          =       images

								return encoded_inputs




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Tuple      )    ->							Dict:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								UpperCAmelCase_      :       Tuple          =       []
								for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]      )

								if len(_A      ) != len(_A      ):
											raise ValueError(
											    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
											    F" {len(_A      )} and {len(_A      )}"      )

								return images_with_overflow




					def 		A      (  self  :		Optional[Any]				, *_A  :		Union[str, Any]				, **_A  :		Union[str, Any]      )    ->							Tuple:
								return self.tokenizer.batch_decode(*_A				, **_A      )




					def 		A      (  self  :		Any				, *_A  :		Optional[Any]				, **_A  :		Tuple      )    ->							Tuple:
								return self.tokenizer.decode(*_A				, **_A      )




					@property
					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								return ["input_ids", "bbox", "attention_mask", "image"]




					@property
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				, _A				, )
								return self.image_processor_class





					@property
					def 		A      (  self  :		Tuple      )    ->							str:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				, _A				, )
								return self.image_processor


